基于用户购买意愿力的协同过滤推荐算法

针对网购行为中商品浏览量排名靠前而销量滞后的问题, 在用户购买意愿力的基础上, 提出一种增强评分矩阵协同过滤推荐算法. 首先, 利用惩罚因子作为增强型矩阵的评价权重, 加权表征用户购物意愿力的商品画像, 取得增强型矩阵的预测评分； 其次, 融合以基于项目的协同过滤推荐, 建立由潜在兴趣商品间的项目相似度矩阵得到的基础型评分矩阵； 最后, 以TOP-N结果向购买意愿较强的目标用户推荐排名靠前的商品. 实验结果表明: 与传统基于项目的协同过滤推荐算法相比, 增强评分矩阵协同过滤推荐算法的推荐准确率提升2.48%, 召回率提升4.31%, 综合值F₁提升3.19%, 从而有效解决了用户感兴趣商品排名靠后, 且不被购买或购买次数较少的问题, 以达到购买意愿力较强、 目标用户更准的推荐宗旨, 进而提高推荐精度.

Collaborative Filtering Recommendation Algorithm Based on Purchasing Intention of Users

Aiming at the problem of high ranking of commodity views and lagging sales in online shopping, based on purchasing intention of users, we proposed a collaborative filtering recommendation algorithm merged with enhanced rating matrix. Firstly, the penalty factor was used as the evaluation weight of the enhanced matrix, and the commodity portrait representing the user’s purchasing intention was weighted to obtain the prediction score of the enhanced matrix. Secondly, the basic rating matrix was obtained by combining the item-based collaborative filtering recommendation to establish the item similarity matrix between the potentially interested commodities. Finally, TOP-N result was used to recommend the top ranked commodities to target users with strong purchasing intention. The experimental results show that, compared with the traditional item-based collaborative filtering recommendation algorithm, the recommendation accuracy of the enhanced rating matrix collaborative filtering recommendation algorithm is improved by 2.48%, the recall rate is improved by 4.31%, and the comprehensive value F₁ is improved by 3.19%, which effectively solves the problem that the commodities of interest to users are ranked low and are not purchased or purchased less times, so as to achieve recommendation purpose of more accurate target users with strong purchasing intention, and then improve the recommendation accuracy.